An Investigation of the Learning and Forgetting of Workers in Dual Resource Constrained Systems
Bibliographic record
Abstract
This thesis examines worker learning and forgetting in dual resource constrained systems according to the dual-phase learning-forgetting model (DPLFM). The contributions are as follows: (1) equations were developed that output controllable shop factors such as training and transfer policies given existing factors such as the degree of job similarity, processing times, and the learning and forgetting rate of the worker, (2) results suggest that the task-type factor with respect to the worker learning rate and proportion of cognitive and motor elements is a factor to include in DRC research, and (3) the results have suggested that the DPLFM emphasized a greater benefit for upfront training and more frequent transfer policy than the learn forget curve model (LFCM) when tasks are similar, and supported the conclusions of Jaber et al. (2003) by an even greater extent that it is possible to use more flexibility in DRC shops with similar tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".